VPIN calculation using bulk-volume classification

VPIN

#### VPIN calculation #########################################################
#install.packages('fasttime',repos='http://www.rforge.net/')
require(data.table); require(fasttime); require(plyr)
# Assuming TAQ data is arranged in 1 year stock csv files
stock=fread('/TAQ_data.csv'); stock=stock[,1:3,with=FALSE]
setnames(stock,colnames(stock),c('DateTime','Price','Volume'));
stock[,DateTime:=paste(paste(substr(DateTime,1,4),substr(DateTime,5,6),
    substr(DateTime,7,8),sep='-'),substr(DateTime,10,17))]
setkey(stock,DateTime);
stock=as.xts(stock[,2:3,with=FALSE],unique=FALSE,
             order.by=fastPOSIXct(stock[,DateTime],tz='GMT'))
# Now we have an xts data frame called 'stock' with a DateTime index and... 
# two columns: Price and Volume
# Vbucket=Number of volume buckets in an average volume day (Vbucket=50)
VPIN=function(stock,Vbucket) {
  stock$dP1=diff(stock[,'Price'],lag=1,diff=1,na.pad=TRUE)
  ends=endpoints(stock,'minutes')
  timeDF=period.apply(stock[,'dP1'],INDEX=ends,FUN=sum)
  timeDF$Volume=period.apply(stock[,'Volume'],INDEX=ends,FUN=sum)
  Vbar=mean(period.apply(timeDF[,'Volume'],INDEX=endpoints(timeDF,'days'),
    FUN=sum))/Vbucket
  timeDF$Vfrac=timeDF[,'Volume']/Vbar
  timeDF$CumVfrac=cumsum(timeDF[,'Vfrac'])
  timeDF$Next=(timeDF[,'CumVfrac']-floor(timeDF[,'CumVfrac']))/timeDF[,'Vfrac']
  timeDF[timeDF[,'Next']<1,'Next']=0
  timeDF$Previous=lag(timeDF[,'dP1'])*lag(timeDF[,'Next'])
  timeDF$dP2=(1-timeDF[,'Next'])*timeDF[,'dP1'] + timeDF[,'Previous']
  timeDF$Vtick=floor(timeDF[,'CumVfrac'])
  timeDF[,'Vtick']=timeDF[,'Vtick']-diff(timeDF[,'Vtick']); timeDF[1,'Vtick']=0
  timeDF=as.data.frame(timeDF); timeDF[,'DateTime']=row.names(timeDF)
  timeDF=ddply(as.data.frame(timeDF),.(Vtick),last)
  timeDF=as.xts(timeDF[,c('Volume','dP2','Vtick')],
    order.by=fastPOSIXct(timeDF$DateTime,tz='GMT'))
  timeDF[1,'dP2']=0
  timeDF$sigma=rollapply(timeDF[,'dP2'],Vbucket,sd,fill=NA)
  timeDF$sigma=na.fill(timeDF$sigma,"extend")
  timeDF$Vbuy=Vbar*pnorm(timeDF[,'dP2']/timeDF[,'sigma'])
  timeDF$Vsell=Vbar-timeDF[,'Vbuy']
  timeDF$OI=abs(timeDF[,'Vsell']-timeDF[,'Vbuy'])
  timeDF$VPIN=rollapply(timeDF[,'OI'],Vbucket,sum)/(Vbar*Vbucket)
  timeDF=timeDF[,c('VPIN')]; return(timeDF)
}
out=VPIN(stock,50)
###############################################################################

Hi, can you repeat flash crash figure in Easley, ohara's 2012 paper? It seems that your code is quite sensitive to the parameter Vbucket? VPIN doesn't increase even in the flash crash time using Emini SP500 data.

@gstar1990，ELO(2012) calculate $std$ of $\delta_p$ with the whole sample, that is why their VPIN is sensitive in the flash crash

Would you explain what does "Next", "Previous" and "dP2" mean?
Thanks a lot!

Hi can you please suggest that can we use this methodology and code if one has data aggregated to 1 minute frequency ?